The Latest Dose

Artificially intelligent tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry. As the drug discovery and preclinical stages speed up and potentially produce more drugs to test in the clinical trial phase, how do clinical researchers prepare and respond to these challenging opportunities?   In this episode, Toban Zolman, Chief Executive Officer at Kivo will share his thoughts on how AI-enabled successes in drug discovery will affect clinical operations and regulatory operations. We will discuss how advancements in technology and data analysis are reshaping the way we conduct clinical research.   --------------------------------------------------------   Episode Transcript: http://traffic.libsyn.com/thelatestdose/The_Latest_Dose-S01_E43.mp3   00;00;00;00 - 00;00;40;25 Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Traditionally drug discovery is a notoriously time consuming and expensive process. A host of artificial intelligence tools, AI, are said to be revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry.   00;00;41;02 - 00;01;11;13 According to the Boston Consulting Group, as of March 2022, “ biotech companies are using an AI first approach had more than 150 small molecule drugs in discovery and more than 15 already in clinical trials”. Once the drug discovery and preclinical stages speed up and potentially produce more drugs to test in the clinical trial phase, how do we prepare and respond to this exciting new and challenging opportunity?   00;01;11;15 - 00;01;42;17 Today, our guest will share his thoughts on how AI enabled successes in drug discovery will affect clinical operations and regulatory operations. We will discuss how advancements in technology and data analysis are reshaping the way we conduct clinical research. Joining me today is Toban Zolman, Chief Executive Officer of Kivo. Toban has 20 years of experience in regulatory and clinical operations, drafting some of the first guidelines for electronic submission at Image Solutions.   00;01;42;19 - 00;02;09;06 Toban has consulted with 45 of the top 50 pharma companies in the world. After working in regulatory, Toban ran product teams for several tech companies. Toban has been at the forefront of multiple tech revolutions, such as cloud computing and the Internet of Things. Toban thinks the time has come for clinical trial management to level up. Toban, it is great to speak with you today.   00;02;09;06 - 00;02;45;17 Welcome to the Latest Dose. Yeah, thank you. Great to speak with you as well. In the intro I mentioned that you believe the time has come for clinical trial management to level up. What do you mean by that? Well, let me give you some context maybe on where that comment is coming from. So, I spent a chunk of my career helping tier one pharma transition to electronic submissions and kind of the promise of electronic submissions was improved process, improved visibility, faster review times by regulatory agencies.   00;02;45;19 - 00;03;22;16 And the way that we went about that as an industry, you know, 15 to 20 years ago, was really to take this new challenge, process challenge, of managing a ten X increase in the amount of documents going back and forth to a regulatory agency and controlling that incredibly tightly. And so literally, you know, I spent years and in windowless conference rooms with committees trying to figure out how to manage every aspect of increasingly complex process.   00;03;22;18 - 00;04;03;13 And honestly, it was soul crushing. So, I left the industry and spent over a decade working in other industries that were kind of on the edge of major transformations. E-commerce, social, cloud, IoT, and eventually circled back to life sciences. And I think the thing that struck me the most as I came back into life sciences and started to talk to clinical and regulatory leaders who were dealing with all of these advancements in how clinical trials operate, as this was kind of the same song, new verse.   00;04;03;16 - 00;04;32;03 The pace of clinical trials was accelerating. The complexity of tools was increasing. And the number of assets that they were having to manage that resulted from those advancements was also increasing.  And the approach to managing all of that was to just have leaders in life sciences, you know, these pharma companies just literally tighten their grip on the process even more.   00;04;32;05 - 00;05;14;08 And that's just not a model that works, and it's not a model that any other industry has embraced. And so, really, I think what we've really focused on, at Kivo, is helping companies loosen their control a little bit, not control of process, but really trying to manage everything in a monolithic top down approach and instead move to more nimble, more decentralized, more collaborative processes to manage this massive increase in the amount of activity that's happening in the clinical pipeline.   00;05;14;10 - 00;05;41;16 Well, welcome back to the life sciences. So, you mentioned how these individuals are sort of holding on to the existing process. So, in preparation for this episode, I read a number of articles and they continued to talk about how pharmaceutical industry resists adopting digital tools, the need for them to change their strategic priorities, and also evolving the work place culture, perhaps in some of the ways you just mentioned.   00;05;41;18 - 00;06;08;08 What are your thoughts about these statements now that you're back? This is true? Are you seeing something else? What do you mean by that? Yeah, great question. So, yeah, I think you're correct in kind of meta level trends.  Life sciences and especially folks that work in operations, whether that's clin ops, reg ops, etc., that are a very risk averse group of people and for good reason.   00;06;08;12 - 00;06;37;11 I'm not throwing shade on anyone. The nature of those jobs and their remit within the drug development process is fundamentally to be risk adverse, and that's what helps create safety in drugs. With that said, you know, Kivo is focused pretty much exclusively on working with emerging life science companies. And so, the vast majority of our customers do not have a drug in market yet.   00;06;37;11 - 00;07;14;04 They have active clinical pipelines, but they are new companies, new in life science terms.  Many are 15 years old. But I think they are hitting growth inflection points really in a post pandemic world. And that's been super fascinating to be involved in because I think these smaller companies that are growing rapidly and hitting inflection points post-pandemic are really leaning into decentralized teams and maybe not even by choice.   00;07;14;04 - 00;07;48;02 It's just the nature of how you scale a company now. But they're leaning into that workplace culture of small, decentralized teams, relying heavily on partners; whether that's CROs, contract medical writers, reg affairs shops, whatever it is. And they are figuring out how to scale organizationally, to scale technologically, and scale as well, their clinical trial process in that landscape.   00;07;48;04 - 00;08;21;19 And so, the conversations we have with leaders in those companies who are really building the organization from the ground up, differ significantly from the conversations we have with companies that reached a scale point, you know, a decade ago or even pre pre-pandemic. Where the workplace culture was centered around in-person, everyone working in the same office sort of a culture.   00;08;21;21 - 00;08;51;24 And so, the industry is risk adverse. Ops folks are risk adverse. The customers we work with that are most successful are the ones that are baking into their corporate culture from the ground up, a more nimble, decentralized approach to managing this influx of data. So that makes sense to me about companies that are coming into the market a lot around the post pandemic and getting more decentralized.   00;08;51;27 - 00;09;14;12 But there, I still think there's a disparity that I'd love to get your thoughts on. So, we talk about AI, the promise, the culture, but we also see that we've had cloud around for more than 20 years. But there are some people that say in some articles that say that 50% of clinical trials are still utilizing paper processes somewhere in it.   00;09;14;15 - 00;09;41;23 So how do we deal with this disparity? How do these large companies deal with this? What are your thoughts on what they need to do? I think our experience aligns to that as well. Even with smaller companies, you know, half of our customers have some sort of paper element that they are navigating. I would frame the conversation about AI and cloud, this way.   00;09;41;26 - 00;10;22;18 Cloud and life sciences is very different than cloud in other industries. The majority of the incumbents, software vendors, especially that are offering part 11 compliant solutions software, that's used deep in the regulated process are they may be cloud based, but this is technology that was created before the iPhone was invented. And so, the paradigm in which a lot of these platforms use is not fundamentally changed from software and processes that were developed in the nineties and early 2000.   00;10;22;20 - 00;11;08;15 AI as a layer on top of that, creates so much acceleration, increase data process challenges, that those two are never going to play well together. So, I think what you are starting to see in the industry is kind of, it's almost like, you know, looking at geology where you've got three strata of incompatible technology.  You've got paper on top of that, you have S

Artificial intelligence (AI) is one of the most discussed technologies across all industries. Life science professionals working in the pharmaceutical industry strive to improve people’s lives tackling incredibly complex diseases. Drug development is often perceived as slow. As the pharma industry looks to improve the drug development process AI promises nothing less than a revolution.   Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists?   Will AI–designed medicines be safe for people? Have the desired effect on the disease? Meet the rigorous regulatory standards to actually be approved for human use?   In this episode, Andreas Busch, Ph.D., Chief Innovation Officer at Absci will answer these questions and shares the value generative-AI is providing drug development today.   --------------------------------------------------------   Episode Transcript: 00;00;00;00 - 00;00;31;24 Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Artificial Intelligence, AI, is one of the most popular technologies on the planet, and I find it referenced in most, if not all, industries.   00;00;31;26 - 00;00;59;16 Those of us working in the pharmaceutical industry strive to improve people's lives. Can AI help scientists develop better medicines faster? Human bodies are incredibly complex. Drug development is slow. Since I've been engaged in drug development, many people, teams, organizations, and companies have been working tirelessly to improve the drug development process, the promise, is nothing more than a revolution for the pharmaceutical industry.   00;00;59;19 - 00;01;26;21 The March 8th, 2023 Politico article states “nearly 270 companies are working in AI driven drug discovery”.  Let's start learning more about AI driven drug discovery and discuss if or when the promise of AI will be realized.  Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists?   00;01;26;23 - 00;02;02;02 Can AI-designed medicines, be safe for people? Have the desire effect on the disease?  Meet the rigorous regulatory standards to actually be approved for human use? You know, many of these questions can be answered today with my guest, Andreas Busch, Ph.D. Chief Information Officer at Absci.  Andreas brings substantial R&D expertise to Absci’s leadership, a world renowned leader in drug discovery and has led R&D efforts for some of the globe's top pharma companies, including Sanofi, Bayer, and Shire.   00;02;02;05 - 00;02;37;05 Andreas’ leadership has resulted in over ten commercial drugs starting from bench to FDA approval, with several more in late stage clinical development. Andreas holds the title of Extraordinary Professor of Pharmacology at the Johann Wolfgang Goethe University in Frankfurt, Germany, where he also received his Ph.D. in pharmacology. Andreas loves, real football a.k.a soccer, enjoys riding his motorcycle through Alps and playing with his beloved dogs Zorro.   00;02;37;07 - 00;03;04;28 Welcome, Andreas. Thank you for making the time to speak with me today. Hey, it's a pleasure talking to you Katherine. So, Andreas I have been taught that artificial intelligence, referred to as AI, are computer intelligence programs that can handle real-time problems and help organizations and everyday people achieve their goal. And AI is obviously a topic of discussion these days and getting way more attention with the release of the articles around ChatGPT.   00;03;04;28 - 00;03;33;22 Today I'd like to focus our discussion on generative AI, but I thought it would be helpful if you could share with me what's important for me to actually know about this type of AI. I'm glad to talk about it. I guess ChatGPT was certainly a breakthrough in AI and the use of AI for a general population and everybody knows now what AI can do through a GPT.   00;03;33;26 - 00;04;07;07 And if you look at generative AI, what we're trying to accomplish simply is to have artificial intelligence supporting us, creating drugs. And as you know, with ChatGPT, you have to give ChatGPT the right prompt in order to get ChatGPT to do the job for you. And this is similar with our generative AI. We need to give the prompt, which is we need to give our models the target, the mechanism we want to work on.   00;04;07;10 - 00;04;43;12 And then the model produces for us, in our case for Absci, a de novo designed antibody. So that's fascinating. How long have you been developing this approach with these prompts and these programs and actually been using this at your organization? I mean, Absci is actually a company which started as a cell line development company and realized then that for AI to be very productive, you need a ton of data and you need a ton of very consistent, high quality data.   00;04;43;14 - 00;05;14;24 So, these two things have to come together, you know, improvement of AI models, but feeding the AI models with plenty of data. So, the models can get better and better. And we've started really implementing AI for our E.coli expression systems for antibody a bit more than two years ago. And the progress we saw in our generative AI approaches were really very significant, very fast.   00;05;14;26 - 00;05;57;16 Already a year ago we were at a stage that we could optimize existing antibodies, so we basically gave the model the information of … look here is a known antibody, …. can you optimize it for affinity, … can you optimize it for immunogenicity and so forth.  And we managed to do that. And just half a year ago, for the first time,  give the model the information of the structure of a protein  that we wanted to address, to produce for us a binding sequence completely de novo or without any idea of an antibody structure before.  I think there was …. really for us …. the breakthrough.   00;05;57;19 - 00;06;28;16 And that is something which we have meanwhile even further progressed in the last half year. We extended this approach to more than one binding regions and we are ready now in a situation to address three of the binding regions of an antibody. And we are very, very optimistic that this progress is going to be extremely meaningful and helpful and what we believe disruptive in biologics research in the future.   00;06;28;18 - 00;06;49;01 So, this is exciting and extremely fascinating. So, I'm going to go to a statement you made about the data. So, can we talk a little bit about that? So where do these sources of data come from? What types of volume are you talking about? And I guess more importantly, as somebody who has worked with data for many, many years,   00;06;49;01 - 00;07;11;00 and one of the things that people will often ask about is ….should you use that data? Is that data appropriate? Is it reliable? Some people use the word quality. So, in order to achieve these impressive results, can you tell us a little bit about, more about, the data that's being used? Where does it come from and all those things?   00;07;11;03 - 00;07;36;13 Sure. To make it clear, what we're doing is, once we know the structure of a mechanism we want to address, let's assume whatever a membrane protein like a G protein coupled receptor, whatever you name it, we identify the region to which we want our antibody to bind and we give this information in the structure of this region to the model.   00;07;36;14 - 00;08;08;25 The model then delivers to us a number of model hits.  Artificial intelligence generated hits.  Information about what the model thinks the binder should look like.  And what we do then, and that's the very straightforward answer to your question of the quality, is we generate those hits in the laboratory, we express the genes relevant for those binding regions in our expression system.   00;08;08;27 - 00;08;42;06 That's a microbial expression system, E coli. And then we simply have a test available called the Ace assay, in which we then validate what is indeed the binding affinity of those calculated binder. So that gives us then immediately an experimental validation of the AI suggestions and of the AI results. And therefore, we feel very, very comfortable that of course the quality of our predictions is very high as we validate them right afterwards.   00;08;42;08 - 00;09;25;10 Not only that, we validate them, but we can then again also use the information of those data to further improve the model. You ask, how many data do we generate? Well, the nice thing about E coli is that it replicates very, very fast and we can express huge libraries. The libraries again are the genes suggested by the model, and we can express easily your libraries of 500,000 or 1 million binding regions and as a consequence can measure 2-3 million of individual binders in a week or two.   00;09;25;10 - 00;09;57;08 And we can of course, also then see how well those binders are expressed in the cells and can measure up to a billion data points and protein interactions per week. Okay. So, I have to ask,  if you didn't have the generative AI and the capabilities that you've just talked about, how long would it take for a human to do this without these additional tools and capabilities?   00;09;57;10 - 00;10;28;20 I think the really exciting piece about what I'm describing to you is that the model not only spits out a binder of a certain quality, but it spits out, already something which we can in a multidimensional way, optimize. So, if you go back to a traditional way of how to generate an antibody, which would be through mouse immunization or rapid immunization or what is called a phage display, you also can get a binder.   00;10;28;20 - 00;11;08;21 However, that binder comes without any potential optimization you would want to see. For example, you know, you get a binder. 

Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. President Biden has reignited the Cancer Moonshot initiative and set a new national goal: “if we work together, we can cut the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and their families living with and surviving cancer”. “To achieve [the cancer moonshot goals], we must amplify digital innovation,” stated Dr. Catharine Young, Assistant Director of Cancer Moonshot Engagement and Policy, White House Office of Science and Technology.   CancerX, an initiative to rapidly accelerate the pace of cancer innovation in the U.S., will harness the power of innovation to reduce the burden of cancer for all people. Oracle is excited and honored to join Cancer Moonshot's new CancerX public-private partnership.   In this episode Jennifer Goldsack, Chief Executive Officer at Digital Medicine Society (DiMe), Santosh Mohan, Vice President, Digital at Moffitt Cancer Center with Moffitt Cancer Center, and Stephen Konya, Senior Advisor to the Deputy National Coordinator, and Innovation Portfolio Lead for the Office of the National Coordinator for Health IT (ONC) will share more about Cancer Moonshot, CancerX and the importance of digital innovation to achieve the goals.   --------------------------------------------------------   Episode Transcript: 00;00;00;00 - 00;00;34;26 Hi, everyone, and welcome to the latest dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020, President Biden has reignited the Cancer Moonshot and set a new national goal.   00;00;34;29 - 00;00;56;27 If we work together, we can cut the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and their families living with and surviving cancer. In response to the White House Cancer Moonshot, CancerX is formed, an initiative to rapidly accelerate the pace of cancer innovation in the United States.   00;00;57;00 - 00;01;26;12 CancerX will harness the power of innovation to reduce the burden of cancer for all people. Oracle is excited and honored to join Cancer's Moonshot New CancerX Public Private Partnership. Here with me today to share more about these inspirational initiatives, our Jennifer Goldsack, Santosh Mohan, and Stephen Konya. Jennifer, Jen, Goldsack is the CEO of the Digital Medicine Society, also known as DIME.   00;01;26;15 - 00;01;56;08 Jen's research focuses on applied approaches to the safe, effective, and equitable use of digital technologies to improve health, health care and health research. Jen is a member of the roundtable on Genetics and Precision Health at the National Academies of Science, Engineering and Medicine. Jen serves on the World Economic Forum Global Leadership Council on Mental Health. Previously, Jen spent several years developing and implementing projects with Clinical Trials Transformation Initiative, also known as CTTI.   00;01;56;10 - 00;02;26;08 This is a public private partnership co-founded by Duke University and the FDA. Jen conducted research at the hospital of the University of Pennsylvania, helped launch the Value Institute, a pragmatic research and innovation center embedded in the large academic medical center in Delaware. Jen earned her master's degree in chemistry from the University of Oxford, England, her master's in history and sociology of medicine from the University of Pennsylvania and her MBA from George Washington University.   00;02;26;10 - 00;03;04;24 Jen is a retired athlete, formerly a Pan American Games champion, Olympian, and world champion silver medalist. Santosh Mohan, vice president of digital at Moffitt Cancer Center, is also with us today. Santosh brings more than 15 years of digital health and health information technology experience to this role. Previously, he served as the managing director of the Innovation Hub at Brigham and Women's Hospital, where he led digital transformation through the use, development, evaluation and commercialization of digital health applications.   00;03;04;27 - 00;03;34;27 Throughout his career, Santosh has worked to leverage data and analytics to create and design new programs and digital abilities, with a strong focus on emerging technology to advance care and improve the clinician and patient experience. Santosh holds a master’s degree in clinical informatics from Duke University’s Fuqua School of Business and a bachelor’s degree in bioinformatics from Vellore Institute of Technology in India.   00;03;34;29 - 00;04;11;01 Santosh is a certified professional in healthcare information and Management Systems, a member of American Medical Informatics Association, a senior member and fellow of the Healthcare Information and Management Systems Society, known also as HIMMS. You will also hear from Stephen Konya, the senior advisor to the Deputy National Coordinator and the Innovation Portfolio Lead for the Office of the National Coordinator for Health I.T., also known as ONC, which is part of the U.S. Department of Health and Human Services, HHS.   00;04;11;03 - 00;05;04;06 Stephen is shaping the agency's long term strategy. The primary liaison to the White House Office of Science and Technology Policy. The primary liaison to the external health care startup and investor community. Stephen leads the Digital Health Innovation Workgroup under the Federal Health I.T. Coordinating Council, an interagency collaboration community comprised of innovation representatives from 40 other federal agencies. Previously, Stephen has led several key ONC projects, including the HHS Pandemic X Innovation Accelerator, the National Health I.T. Playbook, the Agency Patient Engagement Playbook for Providers, the Smart App Gallery, the FHIR at Scale Task Force, also known as FAST, and is a founding co-chair of the Together.Health Collaborative Effort.  Prior to  his position with the federal government,   00;05;04;07 - 00;05;35;07 Stephen served the state of Illinois in a variety of key positions and diverse responsibilities. Stephen holds a BBA in finance and international business from Loyola, University of Chicago, is fellow and mentor of the Mid-American Regional Public Health Leadership Institute Program at the University of Illinois-Chicago School of Public Health. Welcome, Jen, Santosh and Stephen to the Latest Dose and thank you so much for making time to speak with me today.   00;05;35;10 - 00;06;01;03 When I hear the word cancer, it elicits fear and anxiety, at least in me. So, researching the cancer trends does not provide me with much comfort. According to the World Health Organization, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. Or stated another way, nearly one in six deaths. It appears that the medical community's understanding of cancer is growing,   00;06;01;06 - 00;06;27;21 yet the death rate remains so high. What do we need to do differently? Thanks, Katherine. Cancer is out every day at Moffitt. We come face to face with this terrible, very difficult disease. Every single day. But we also see the courage of our patients fighting it. And that really inspires us to bring hope to every patient we serve and deliver to them some of the best outcomes.   00;06;27;22 - 00;06;52;26 Up to four times the national average. Now, cancer deaths in the US are actually falling, but they're not falling fast enough so the death rate needs to decline by a more rapid percentage to reach the Moonshot goal of reducing cancer deaths by 50% in the next 25 years.  It's very clear that we need a multifaceted approach to tackle this complex issue.   00;06;52;29 - 00;07;21;18 First and foremost, prevention and early detection must be at the forefront, emphasizing lifestyle and behavior changes, like adopting a healthy diet, regular exercise, smoking cessation. All of these can significantly reduce cancer risks. Equally important is promoting awareness about the importance of regular screenings and recognizing early signs and symptoms. And we know that screening rates have declined for all cancers since the pandemic started.   00;07;21;18 - 00;07;48;28 So, we will likely soon start seeing cancers presenting at more advanced stages requiring longer and more complex treatment, as well as decreasing positive outcomes. This means that we need to move engagement upstream and increase those screening rates. And this is where digital channels can help. We've been at the forefront of prevention and screening for years now, and really the reignited   00;07;48;28 - 00;08;13;16 Moonshot has been an opportunity for us to accelerate these efforts around re-energizing the community to prioritize cancer screenings. Early interventions can make a world of difference, but prevention and early detection are just the beginning, and they require a lot of behavioral change within our society. And while we advance that, we should also recognize that cancer will continue to occur.   00;08;13;18 - 00;08;44;06 So, we need to change the trajectory of cancer mortality, not just the incidence with therapeutic advancements, including immunotherapy and especially CAR-T. And therefore, we need to continue investing in cancer research and innovation. And collaboration is really key in this space. Collaboration among academia, with the industry, research institutions and entrepreneurs, it's really vital to expedite progress in this space.   00;08;44;08 - 00;09;11;11 But progress also means nothing if it is not accessible to everyone. And so, ensuring affordable and accessible cancer care is a must. So again, this is another space where organizations must work together to bridge that gap and provide quality care to all individuals

The US cancer death rate has fallen by 33% since 1991 with an estimated 3.8 million deaths averted. This is attributed to “good progress” improvements in cancer treatment, decreases in smoking, and increases in early detection. A recent rise in advanced cancer cases reported is believed to be an outcome of the COVID-19 pandemic which delayed screenings and treatment.   Access, equity, and inclusion when developing and deploying new solutions to combat this disease remain paramount. The impact of cancer on people’s lives and their families is profound. Many live with cancer for long periods and it is important to consider the morbidity caused by this disease. Cancer survivors are 2½ times more likely to declare bankruptcy than those without the disease.   CancerX is responding to the call of the White House by establishing a public-private partnership to boost innovation in the fight against cancer. This initiative brings many diverse stakeholders together to unleash the power of innovation needed to create a future free of the burden of cancer.   In this episode, Sarah Sheehan, Program Lead at the Digital Medicine Society (DiMe), Dr. Corinne Leach, Director of Digital Innovation for Research Excellence with Moffitt Cancer Center, and Dr. Grace Cordovano, co-founder of Unblock Health will unveil the goals and deliverables of the inaugural CancerX project, Advancing Digital Innovation to Improve Equity and Reduce Financial Toxicity in Cancer Care and Research.

The success or failure of clinical trials is dependent in large part on the engagement of the principal investigator (PI). PIs play an important role in trial selection, site activation, and study execution. This includes but is not limited to, the development and implementation of a strategy to maximize enrollment, optimize data quality, and ensure patient retention. The legal, regulatory, financial, and workload burden for site PIs has grown considerably over time. The benefits of serving as a site PI are becoming less evident. As a result, increasing dissatisfaction exists among physicians contributing to trials resulting in decreasing interest in trial participation.   According to the Tufts Center for the Study of Drug Development (Tufts CSDD) just over 32,000 active principal investigators are operating worldwide (as of Dec 2021). This number continues to grow but at a slower overall rate of 1.5% annually during the most recent 10-year period (2010 – 2020) compared to 4.6% annually in the prior decade. However, the number of FDA-registered studies during this same 10-year period grew at an average annual rate of 7%.   In this episode, Dr. Gerald Y. Minuk, Professor Emeritus at the University of Manitoba in Winnipeg, Canada, and CEO of Refuah Solutions will share his recommendations to ease the burden of the principal investigator and support the growth of these important leaders of clinical research.